xgboost

XGBClassifier num_class is invalid

由 梦想的初衷 提交于 2020-06-10 17:45:46
问题 I am using XGBClassifier (in xgboost) for a multi-class classification. Upon executing the classifier, I am receiving an error stating: unexpected keyword argument 'num_class' Code that caused this error is listed below (params is a valid set of parameters for xgb): xgb.XGBClassifier(params, num_class=100) I searched a bit and found that 'num_class' parameter is named 'n_classes' for scikit implementation of XGBClassifier. I tried this change and received a similar error: unexpected keyword

XGBoost with GridSearchCV, Scaling, PCA, and Early-Stopping in sklearn Pipeline

由 夙愿已清 提交于 2020-06-09 11:31:45
问题 I want to combine a XGBoost model with input scaling and feature space reduction by PCA. In addition, the hyperparameters of the model as well as the number of components used in the PCA should be tuned using cross-validation. And to prevent the model from overfitting, early stopping should be added. For combining the various steps, I decided to use sklearn's Pipeline functionalities. At the beginning, I had some problems making sure, that the PCA is also applied to the validation set. But I

Plot a Single XGBoost Decision Tree

由 浪子不回头ぞ 提交于 2020-06-08 07:32:13
问题 I am using method on https://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ to plot a XGBoost Decision Tree from numpy import loadtxt from xgboost import XGBClassifier from xgboost import plot_tree import matplotlib.pyplot as plt # load data dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",") # split data into X and y X = dataset[:,0:8] y = dataset[:,8] # fit model no training data model = XGBClassifier() model.fit(X, y) # plot single tree plot

Plot a Single XGBoost Decision Tree

由 六眼飞鱼酱① 提交于 2020-06-08 07:28:27
问题 I am using method on https://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ to plot a XGBoost Decision Tree from numpy import loadtxt from xgboost import XGBClassifier from xgboost import plot_tree import matplotlib.pyplot as plt # load data dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",") # split data into X and y X = dataset[:,0:8] y = dataset[:,8] # fit model no training data model = XGBClassifier() model.fit(X, y) # plot single tree plot

Plot a Single XGBoost Decision Tree

由 非 Y 不嫁゛ 提交于 2020-06-08 07:28:08
问题 I am using method on https://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ to plot a XGBoost Decision Tree from numpy import loadtxt from xgboost import XGBClassifier from xgboost import plot_tree import matplotlib.pyplot as plt # load data dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",") # split data into X and y X = dataset[:,0:8] y = dataset[:,8] # fit model no training data model = XGBClassifier() model.fit(X, y) # plot single tree plot

Sparse matrix support for long vectors (over 2^31 elements)

由 ε祈祈猫儿з 提交于 2020-05-29 07:37:26
问题 I know this question has been asked in the past (here and here, for example), but those questions are years old and unresolved. I am wondering if any solutions have been created since then. The issue is that the Matrix package in R cannot handle long vectors (length greater than 2^31 - 1). In my case, a sparse matrix is necessary for running an XGBoost model because of memory and time constraints. The XGBoost xgb.DMatrix supports using a dgCMatrix object. However, due to the size of my data,

Port XGBoost model trained in python to another system written in C/C++

由 旧街凉风 提交于 2020-05-25 19:55:11
问题 Suppose I have successfully trained a XGBoost machine learning model in python. x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=7) model = XGBClassifier() model.fit(x_train, y_train) y_pred = model.predict(x_test) I want to port this model to another system which will be writte in C/C++. To do this, I need to know the internal logic of the XGboost trained model and translate them into a series of if-then-else statements like decision trees, if I am not

Loading XGBoost Model: ModuleNotFoundError: No module named 'sklearn.preprocessing._label'

由 馋奶兔 提交于 2020-05-11 07:48:10
问题 I'm having issues loading a pretrained xgboost model using the following code: xgb_model = pickle.load(open('churnfinalunscaled.pickle.dat', 'rb')) And when I do that, I get the following error: ModuleNotFoundError Traceback (most recent call last) <ipython-input-29-31e7f426e19e> in <module>() ----> 1 xgb_model = pickle.load(open('churnfinalunscaled.pickle.dat', 'rb')) ModuleNotFoundError: No module named 'sklearn.preprocessing._label' I haven't seen anything online so any help would be much

Xgboost: what is the difference among bst.best_score, bst.best_iteration and bst.best_ntree_limit?

由 一世执手 提交于 2020-05-10 14:16:13
问题 When I use xgboost to train my data for a 2-cates classification problem ,I'd like to use the early stopping to get the best model, but I'm confused about which one to use in my predict as the early stop will return 3 different choices. For example, should I use preds = model.predict(xgtest, ntree_limit=bst.best_iteration) or should I use preds = model.predict(xgtest, ntree_limit=bst.best_ntree_limit) or both right, and they should be applied to different circumstances? If so, how can I judge

布客·ApacheCN 翻译校对活动进度公告 2020.5

由 纵然是瞬间 提交于 2020-05-09 10:40:04
注意 请贡献者查看参与方式，然后直接在 ISSUE 中认领。 翻译/校对三个文档就可以申请当负责人，我们会把你拉进合伙人群。翻译/校对五个文档的贡献者，可以申请实习证明。 请私聊片刻（529815144）、咸鱼（1034616238）、或飞龙（562826179）来领取以上奖励。 可解释的机器学习【校对】 参与方式： github.com/apachecn/in… 整体进度： github.com/apachecn/in… 项目仓库： github.com/apachecn/in… 认领：7/9，校对：7/9 章节 校对者 进度 前言 @wnma3mz 完成 第一章 引言 @wnma3mz 完成 第二章 解释性 @utopfish 完成 第三章 数据集 @GeneralLi95 完成 第四章 解释模型 第五章 模型不可知论方法 第六章 基于实例的解释 @mahaoyang 完成 第七章 神经网络解释 @binbinmeng 完成 第八章 水晶球 @mahaoyang 完成 UCB DS100 课本：数据科学的原理与技巧【校对】 参与方式： github.com/apachecn/ds… 整体进度： github.com/apachecn/ds… 项目仓库： github.com/apachecn/ds… 认领：7/44，校对：5/44 章节 贡献者 进度 七、Web 技术 - -
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